Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification.

The restoration of karst rocky desertification is reflected not only in the reduction in severely degraded areas but also in the stability of the restoration process and the potential risk of future reversal. Taking southeastern Yunnan, China, as the study area, this study constructed a six-period rocky desertification grade sequence for the years 2000, 2005, 2010, 2015, 2020, and 2024 using Landsat imagery, CLCD land-cover data, and DEM-derived slope constraints. Area change analysis, grade-transition matrices, Sen–MK trend analysis, coefficient of variation (CV), Markov–PLUS scenario simulation, scenario-sensitivity analysis, and PLUS driver contribution assessment were integrated into a process-oriented diagnostic framework to examine rocky desertification recovery from three dimensions: grade-structure adjustment, trend-variability stability, and potential future reversal risk. The results indicate that rocky desertification in southeastern Yunnan generally weakened from 2000 to 2024. The proportion of moderate-and-above rocky desertification decreased from 50.32% to 28.58%, while non-rocky desertification and potential rocky desertification expanded substantially. Grade transitions were dominated by gradual conversions among adjacent classes, with the most evident improvement occurring during 2010–2015, when the proportion of improvement transitions reached 44.16%. The trend-variability analysis indicated that while improvement dominated the study area overall, the northern, northwestern, central mountainous, and parts of the southwestern areas still exhibited relatively strong variability and localized deterioration risk. Hindcast validation showed relatively high map-level consistency between simulated and historical patterns, with an overall accuracy of 0.9354 and a Kappa coefficient of 0.9153. The three-scenario comparison further showed that the proportion of moderate-and-above rocky desertification varied from 28.07% to 29.41% in 2030 and from 26.85% to 29.06% in 2035 under different transition-probability assumptions. Specifically, the ecological restoration enhancement scenario reduced projected moderate-and-above rocky desertification, whereas the degradation pressure scenario increased it relative to the baseline scenario. These findings indicate that rocky desertification recovery in southeastern Yunnan is not a continuous or linear process, but is characterized by stage-specific adjustment, spatial differentiation, and local variability. Therefore, future rocky desertification control should focus not only on reducing high-severity areas, but also on maintaining restoration stability, identifying variability-sensitive transitional zones, and strengthening differentiated management in areas where terrain constraints, land-cover proximity, and historical variability jointly increase reversal risk.